wehub-resource-sync
797 lines
33 KiB
Python
797 lines
33 KiB
Python
import os
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from collections import deque
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from dataclasses import dataclass
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from typing import final
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from lightrag.file_atomic import atomic_write, reap_orphan_tmp_files
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from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
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from lightrag.utils import logger, validate_workspace
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from lightrag.base import BaseGraphStorage
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import networkx as nx
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from .shared_storage import (
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get_namespace_lock,
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get_update_flag,
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set_all_update_flags,
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)
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from dotenv import load_dotenv
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# use the .env that is inside the current folder
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# allows to use different .env file for each lightrag instance
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# the OS environment variables take precedence over the .env file
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load_dotenv(dotenv_path=".env", override=False)
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@final
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@dataclass
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class NetworkXStorage(BaseGraphStorage):
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"""File-backed knowledge-graph storage built on ``networkx.Graph``.
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Storage model:
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A single ``networkx.Graph`` instance lives in process memory; its
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full state is serialized to one GraphML file at
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``working_dir/[workspace/]graph_<namespace>.graphml``. That GraphML
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file is the **only** cross-process synchronization surface — there
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is no shared memory, no message bus, and no network channel
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between processes. Cross-process visibility is mediated by (a) an
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atomic file write at commit time and (b) a per-namespace
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``storage_updated`` flag distributed through
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``lightrag.kg.shared_storage``.
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Concurrency invariants (the code in this file is correct *only* while
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all three hold):
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1. **Single writer per workspace.** The document pipeline's
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``busy`` / ``destructive_busy`` flags (see ``AGENTS.md``
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*Pipeline concurrency contract*) guarantee at most one process
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performs ``upsert_*`` / ``delete_*`` / ``remove_*`` /
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``index_done_callback`` at any time. Every other process is
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read-only.
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2. **Eventual consistency is sufficient.** Read-only processes
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only need to observe the writer's data *after* the writer's
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``index_done_callback`` completes. Reads landing in the gap
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between a writer's in-memory mutation and its commit may
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legitimately return the pre-update snapshot.
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3. **networkx operations are fully synchronous.** Under a
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single-threaded asyncio event loop, ``graph.add_node`` /
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``graph.remove_node`` / ``graph.degree`` / etc. cannot be
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preempted by another coroutine, which gives them implicit
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mutual exclusion over ``self._graph``. This is why the methods
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below don't have to hold ``_storage_lock`` while calling into
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``graph``.
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Cross-process sync protocol (identical in shape to
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``NanoVectorDBStorage`` — see that class's docstring for the canonical
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description):
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Writer side (``index_done_callback``):
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1. ``write_nx_graph`` atomically writes the GraphML file
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(``atomic_write`` lays a tmp file beside the target and
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renames it into place — readers either see the previous
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file in full or the new file in full, never a torn write).
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2. ``set_all_update_flags`` flips every process's
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``storage_updated`` flag (including the writer's own).
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3. Immediately reset the writer's own flag to ``False`` so
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the next call to ``_get_graph`` does not trigger a
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self-reload of the data this process just wrote.
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Reader side (any method that goes through ``_get_graph``):
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1. Inside ``_storage_lock``, observe
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``storage_updated.value is True``.
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2. **Fully reload** ``self._graph`` from disk via
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``load_nx_graph``. networkx GraphML has no incremental
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sync API, so the entire file is re-parsed.
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3. Reset the reader's own flag.
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Lock scope:
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``_storage_lock`` is a per-``(namespace, workspace)`` keyed lock
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spanning both intra-process coroutines and inter-process workers.
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It wraps only the *reload* and *commit* critical sections, not
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every ``graph.xxx`` call. Operating on ``graph`` outside the lock
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is safe today *because of invariant (3)* — if either premise is
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ever broken (e.g. ``graph.xxx`` is moved to a thread pool, or
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networkx is swapped for an async graph library), the lock scope
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must be widened to cover the mutation/read itself.
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Implementation differences from ``NanoVectorDBStorage`` (same design,
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different surface):
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* No ``client_storage`` property — there is no equivalent live
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reference being exposed to callers, so NanoVectorDB's
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"do-not-retain-across-await" caveat does not apply here.
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* ``write_nx_graph`` passes the tmp path directly to
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``nx.write_graphml``, so the writer needs no equivalent of
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NanoVectorDB's "temporarily reassign ``storage_file``" trick.
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* Mutation surface is finer-grained (``upsert_node`` /
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``upsert_edge`` / ``upsert_nodes_batch`` /
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``upsert_edges_batch`` / ``delete_node`` / ``remove_nodes`` /
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``remove_edges``); each goes through ``_get_graph`` once and
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then operates synchronously on ``self._graph``.
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Non-pipeline write paths:
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The pipeline's ``busy`` gate serializes mutation calls reached
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through the document ingestion and purge flows. The following
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entry points are **not** serialized by the pipeline gate and
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must be guarded externally:
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* ``drop`` — currently gated by the API layer (the
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``/documents/clear`` endpoint takes the pipeline busy
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reservation before invoking it).
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* ``delete_node`` / ``remove_nodes`` / ``remove_edges`` /
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``upsert_node`` / ``upsert_edge`` when invoked from
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``utils_graph.py`` admin flows (``adelete_by_entity`` /
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``adelete_by_relation`` / entity-edit flows). These flows
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are currently not exposed in the WebUI; any future caller
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must arrange single-writer serialization the same way the
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pipeline does.
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"""
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@staticmethod
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def load_nx_graph(file_name) -> nx.Graph:
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if os.path.exists(file_name):
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return nx.read_graphml(file_name)
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return None
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@staticmethod
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def write_nx_graph(graph: nx.Graph, file_name, workspace="_"):
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logger.info(
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f"[{workspace}] Writing graph with {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges"
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)
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atomic_write(
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file_name,
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lambda tmp: nx.write_graphml(graph, tmp),
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workspace,
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)
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def __post_init__(self):
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# Reject path traversal before using workspace in a file path
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validate_workspace(self.workspace)
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working_dir = self.global_config["working_dir"]
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if self.workspace:
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# Include workspace in the file path for data isolation
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workspace_dir = os.path.join(working_dir, self.workspace)
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else:
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# Default behavior when workspace is empty
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workspace_dir = working_dir
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self.workspace = ""
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os.makedirs(workspace_dir, exist_ok=True)
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self._graphml_xml_file = os.path.join(
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workspace_dir, f"graph_{self.namespace}.graphml"
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)
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self._storage_lock = None
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self.storage_updated = None
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self._graph = None
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reap_orphan_tmp_files(self._graphml_xml_file, workspace=self.workspace or "_")
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# Load initial graph
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preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
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if preloaded_graph is not None:
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logger.info(
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f"[{self.workspace}] Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
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)
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else:
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logger.info(
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f"[{self.workspace}] Created new empty graph file: {self._graphml_xml_file}"
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)
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self._graph = preloaded_graph or nx.Graph()
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async def initialize(self):
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"""Initialize storage data"""
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# Get the update flag for cross-process update notification
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self.storage_updated = await get_update_flag(
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self.namespace, workspace=self.workspace
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)
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# Get the storage lock for use in other methods
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self._storage_lock = get_namespace_lock(
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self.namespace, workspace=self.workspace
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)
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async def _get_graph(self):
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"""Return the live ``networkx.Graph``, reloading from disk if needed.
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This is the **single entry point** every public method funnels
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through to obtain ``self._graph``. It is also the **only place
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readers transition to a fresher on-disk snapshot**: when another
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process has committed (via ``index_done_callback``) and flipped
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this process's ``storage_updated`` flag, the next call here
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rebuilds ``self._graph`` by re-parsing the entire GraphML file.
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networkx has no incremental sync API — the reload is
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unconditionally a full file reload.
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Under the *Single writer* invariant (see class docstring), the
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reload branch never fires in the writer process: the writer
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resets its own flag at the end of every ``index_done_callback``.
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The branch exists for readers.
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``_storage_lock`` is held during the check-and-reload to (a)
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serialize concurrent reload attempts by sibling coroutines in
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the same process and (b) interlock with ``index_done_callback``
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so a reader cannot observe a partially-saved file.
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"""
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async with self._storage_lock:
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# Check if data needs to be reloaded
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if self.storage_updated.value:
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logger.info(
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f"[{self.workspace}] Process {os.getpid()} reloading graph {self._graphml_xml_file} due to modifications by another process"
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)
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# Reload data
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self._graph = (
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NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
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)
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# Reset update flag
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self.storage_updated.value = False
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return self._graph
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async def has_node(self, node_id: str) -> bool:
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graph = await self._get_graph()
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return graph.has_node(node_id)
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async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
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graph = await self._get_graph()
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return graph.has_edge(source_node_id, target_node_id)
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async def get_node(self, node_id: str) -> dict[str, str] | None:
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graph = await self._get_graph()
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return graph.nodes.get(node_id)
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async def node_degree(self, node_id: str) -> int:
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graph = await self._get_graph()
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if graph.has_node(node_id):
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return graph.degree(node_id)
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return 0
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async def edge_degree(self, src_id: str, tgt_id: str) -> int:
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graph = await self._get_graph()
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src_degree = graph.degree(src_id) if graph.has_node(src_id) else 0
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tgt_degree = graph.degree(tgt_id) if graph.has_node(tgt_id) else 0
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return src_degree + tgt_degree
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async def get_edge(
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self, source_node_id: str, target_node_id: str
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) -> dict[str, str] | None:
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graph = await self._get_graph()
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return graph.edges.get((source_node_id, target_node_id))
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async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
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graph = await self._get_graph()
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if graph.has_node(source_node_id):
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return list(graph.edges(source_node_id))
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return None
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async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
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"""Insert or update a single node; persistence is deferred.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. In ``lightrag.py`` this
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is handled by ``_insert_done()`` at the end of the document
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batch. Callers outside the pipeline must persist explicitly.
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Correctness relies on the class docstring *Lock scope* invariant
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(synchronous networkx ops + single-writer pipeline gate).
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"""
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graph = await self._get_graph()
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graph.add_node(node_id, **node_data)
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async def upsert_edge(
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self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
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) -> None:
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"""Insert or update a single edge; persistence is deferred.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. Callers outside the
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pipeline must persist explicitly.
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Correctness relies on the class docstring *Lock scope* invariant.
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"""
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graph = await self._get_graph()
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graph.add_edge(source_node_id, target_node_id, **edge_data)
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async def upsert_nodes_batch(self, nodes: list[tuple[str, dict[str, str]]]) -> None:
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"""Batch insert/update multiple nodes in a single call.
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Much faster than calling upsert_node() in a loop for large imports
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because it avoids per-call async event loop overhead.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. Callers outside the
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pipeline must persist explicitly.
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Args:
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nodes: List of (node_id, node_data) tuples.
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"""
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graph = await self._get_graph()
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for node_id, node_data in nodes:
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graph.add_node(node_id, **node_data)
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async def has_nodes_batch(self, node_ids: list[str]) -> set[str]:
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"""Check existence of multiple nodes in a single call.
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Returns:
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Set of node_ids that exist in the graph.
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"""
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graph = await self._get_graph()
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return {nid for nid in node_ids if graph.has_node(nid)}
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async def upsert_edges_batch(
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self, edges: list[tuple[str, str, dict[str, str]]]
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) -> None:
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"""Batch insert/update multiple edges in a single call.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. Callers outside the
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pipeline must persist explicitly.
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Args:
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edges: List of (source_id, target_id, edge_data) tuples.
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"""
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graph = await self._get_graph()
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for src, tgt, edge_data in edges:
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graph.add_edge(src, tgt, **edge_data)
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async def delete_node(self, node_id: str) -> None:
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"""Remove a single node from the graph; persistence is deferred.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. Callers outside the
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pipeline must persist explicitly.
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Pipeline-gating depends on the caller: invocations from the
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document purge flow are serialized by ``pipeline busy``;
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invocations from ``utils_graph.py`` admin flows are **not** —
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see class docstring *Non-pipeline write paths*.
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"""
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graph = await self._get_graph()
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if graph.has_node(node_id):
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graph.remove_node(node_id)
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logger.debug(f"[{self.workspace}] Node {node_id} deleted from the graph")
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else:
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logger.warning(
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f"[{self.workspace}] Node {node_id} not found in the graph for deletion"
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)
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async def remove_nodes(self, nodes: list[str]):
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"""Delete multiple nodes from the graph.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. Callers outside the
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pipeline must persist explicitly.
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Pipeline-gating depends on the caller — see ``delete_node`` and
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class docstring *Non-pipeline write paths*.
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Args:
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nodes: List of node IDs to be deleted
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"""
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graph = await self._get_graph()
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for node in nodes:
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if graph.has_node(node):
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graph.remove_node(node)
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async def remove_edges(self, edges: list[tuple[str, str]]):
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"""Delete multiple edges from the graph.
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Persistence:
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Changes are in-memory only; cross-process visibility requires
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a subsequent ``index_done_callback``. Callers outside the
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pipeline must persist explicitly.
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Pipeline-gating depends on the caller — see ``delete_node`` and
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class docstring *Non-pipeline write paths*.
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Args:
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edges: List of edges to be deleted, each edge is a (source, target) tuple
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"""
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graph = await self._get_graph()
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for source, target in edges:
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if graph.has_edge(source, target):
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graph.remove_edge(source, target)
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async def get_all_labels(self) -> list[str]:
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"""
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Get all node labels(entity names) in the graph
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Returns:
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[label1, label2, ...] # Alphabetically sorted label list
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"""
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graph = await self._get_graph()
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labels = set()
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for node in graph.nodes():
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labels.add(str(node)) # Add node id as a label
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# Return sorted list
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return sorted(list(labels))
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async def get_popular_labels(self, limit: int = 300) -> list[str]:
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"""
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Get popular labels(entity names) by node degree (most connected entities)
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Args:
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limit: Maximum number of labels to return
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Returns:
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List of labels sorted by degree (highest first)
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"""
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graph = await self._get_graph()
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# Get degrees of all nodes and sort by degree descending
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degrees = dict(graph.degree())
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sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
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# Return top labels limited by the specified limit
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popular_labels = [str(node) for node, _ in sorted_nodes[:limit]]
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logger.debug(
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f"[{self.workspace}] Retrieved {len(popular_labels)} popular labels (limit: {limit})"
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)
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return popular_labels
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async def search_labels(self, query: str, limit: int = 50) -> list[str]:
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"""
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Search labels(entity names) with fuzzy matching
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Args:
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query: Search query string
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limit: Maximum number of results to return
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Returns:
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List of matching labels sorted by relevance
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"""
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graph = await self._get_graph()
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query_lower = query.lower().strip()
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if not query_lower:
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return []
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# Collect matching nodes with relevance scores
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matches = []
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for node in graph.nodes():
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node_str = str(node)
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node_lower = node_str.lower()
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# Skip if no match
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if query_lower not in node_lower:
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continue
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# Calculate relevance score
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# Exact match gets highest score
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if node_lower == query_lower:
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score = 1000
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# Prefix match gets high score
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elif node_lower.startswith(query_lower):
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score = 500
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# Contains match gets base score, with bonus for shorter strings
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else:
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# Shorter strings with matches are more relevant
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score = 100 - len(node_str)
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# Bonus for word boundary matches
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if f" {query_lower}" in node_lower or f"_{query_lower}" in node_lower:
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score += 50
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matches.append((node_str, score))
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# Sort by relevance score (desc) then alphabetically
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matches.sort(key=lambda x: (-x[1], x[0]))
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# Return top matches limited by the specified limit
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search_results = [match[0] for match in matches[:limit]]
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logger.debug(
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f"[{self.workspace}] Search query '{query}' returned {len(search_results)} results (limit: {limit})"
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)
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return search_results
|
||
|
||
async def get_knowledge_graph(
|
||
self,
|
||
node_label: str,
|
||
max_depth: int = 3,
|
||
max_nodes: int = None,
|
||
) -> KnowledgeGraph:
|
||
"""
|
||
Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
|
||
|
||
Args:
|
||
node_label: Label of the starting node，* means all nodes
|
||
max_depth: Maximum depth of the subgraph, Defaults to 3
|
||
max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
|
||
|
||
Returns:
|
||
KnowledgeGraph object containing nodes and edges, with an is_truncated flag
|
||
indicating whether the graph was truncated due to max_nodes limit
|
||
"""
|
||
# Get max_nodes from global_config if not provided
|
||
if max_nodes is None:
|
||
max_nodes = self.global_config.get("max_graph_nodes", 1000)
|
||
else:
|
||
# Limit max_nodes to not exceed global_config max_graph_nodes
|
||
max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000))
|
||
|
||
graph = await self._get_graph()
|
||
|
||
result = KnowledgeGraph()
|
||
|
||
# Handle special case for "*" label
|
||
if node_label == "*":
|
||
# Get degrees of all nodes
|
||
degrees = dict(graph.degree())
|
||
# Sort nodes by degree in descending order and take top max_nodes
|
||
sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
|
||
|
||
# Check if graph is truncated
|
||
if len(sorted_nodes) > max_nodes:
|
||
result.is_truncated = True
|
||
logger.info(
|
||
f"[{self.workspace}] Graph truncated: {len(sorted_nodes)} nodes found, limited to {max_nodes}"
|
||
)
|
||
|
||
limited_nodes = [node for node, _ in sorted_nodes[:max_nodes]]
|
||
# Create subgraph with the highest degree nodes
|
||
subgraph = graph.subgraph(limited_nodes)
|
||
else:
|
||
# Check if node exists
|
||
if node_label not in graph:
|
||
logger.warning(
|
||
f"[{self.workspace}] Node {node_label} not found in the graph"
|
||
)
|
||
return KnowledgeGraph() # Return empty graph
|
||
|
||
# Use modified BFS to get nodes, prioritizing high-degree nodes at the same depth
|
||
bfs_nodes = []
|
||
visited = set()
|
||
# Store (node, depth, degree) in the queue
|
||
queue = deque([(node_label, 0, graph.degree(node_label))])
|
||
|
||
# Flag to track if there are unexplored neighbors due to depth limit
|
||
has_unexplored_neighbors = False
|
||
|
||
# Modified breadth-first search with degree-based prioritization
|
||
while queue and len(bfs_nodes) < max_nodes:
|
||
# Get the current depth from the first node in queue
|
||
current_depth = queue[0][1]
|
||
|
||
# Collect all nodes at the current depth
|
||
current_level_nodes = []
|
||
while queue and queue[0][1] == current_depth:
|
||
current_level_nodes.append(queue.popleft())
|
||
|
||
# Sort nodes at current depth by degree (highest first)
|
||
current_level_nodes.sort(key=lambda x: x[2], reverse=True)
|
||
|
||
# Process all nodes at current depth in order of degree
|
||
for current_node, depth, degree in current_level_nodes:
|
||
if current_node not in visited:
|
||
visited.add(current_node)
|
||
bfs_nodes.append(current_node)
|
||
|
||
# Only explore neighbors if we haven't reached max_depth
|
||
if depth < max_depth:
|
||
# Add neighbor nodes to queue with incremented depth
|
||
neighbors = list(graph.neighbors(current_node))
|
||
# Filter out already visited neighbors
|
||
unvisited_neighbors = [
|
||
n for n in neighbors if n not in visited
|
||
]
|
||
# Add neighbors to the queue with their degrees
|
||
for neighbor in unvisited_neighbors:
|
||
neighbor_degree = graph.degree(neighbor)
|
||
queue.append((neighbor, depth + 1, neighbor_degree))
|
||
else:
|
||
# Check if there are unexplored neighbors (skipped due to depth limit)
|
||
neighbors = list(graph.neighbors(current_node))
|
||
unvisited_neighbors = [
|
||
n for n in neighbors if n not in visited
|
||
]
|
||
if unvisited_neighbors:
|
||
has_unexplored_neighbors = True
|
||
|
||
# Check if we've reached max_nodes
|
||
if len(bfs_nodes) >= max_nodes:
|
||
break
|
||
|
||
# Check if graph is truncated - either due to max_nodes limit or depth limit
|
||
if (queue and len(bfs_nodes) >= max_nodes) or has_unexplored_neighbors:
|
||
if len(bfs_nodes) >= max_nodes:
|
||
result.is_truncated = True
|
||
logger.info(
|
||
f"[{self.workspace}] Graph truncated: max_nodes limit {max_nodes} reached"
|
||
)
|
||
else:
|
||
logger.info(
|
||
f"[{self.workspace}] Graph truncated: found {len(bfs_nodes)} nodes within max_depth {max_depth}"
|
||
)
|
||
|
||
# Create subgraph with BFS discovered nodes
|
||
subgraph = graph.subgraph(bfs_nodes)
|
||
|
||
# Add nodes to result
|
||
seen_nodes = set()
|
||
seen_edges = set()
|
||
for node in subgraph.nodes():
|
||
if str(node) in seen_nodes:
|
||
continue
|
||
|
||
node_data = dict(subgraph.nodes[node])
|
||
# Get entity_type as labels
|
||
labels = []
|
||
if "entity_type" in node_data:
|
||
if isinstance(node_data["entity_type"], list):
|
||
labels.extend(node_data["entity_type"])
|
||
else:
|
||
labels.append(node_data["entity_type"])
|
||
|
||
# Create node with properties
|
||
node_properties = {k: v for k, v in node_data.items()}
|
||
|
||
result.nodes.append(
|
||
KnowledgeGraphNode(
|
||
id=str(node), labels=[str(node)], properties=node_properties
|
||
)
|
||
)
|
||
seen_nodes.add(str(node))
|
||
|
||
# Add edges to result
|
||
for edge in subgraph.edges():
|
||
source, target = edge
|
||
# Esure unique edge_id for undirect graph
|
||
if str(source) > str(target):
|
||
source, target = target, source
|
||
edge_id = f"{source}-{target}"
|
||
if edge_id in seen_edges:
|
||
continue
|
||
|
||
edge_data = dict(subgraph.edges[edge])
|
||
|
||
# Create edge with complete information
|
||
result.edges.append(
|
||
KnowledgeGraphEdge(
|
||
id=edge_id,
|
||
type="DIRECTED",
|
||
source=str(source),
|
||
target=str(target),
|
||
properties=edge_data,
|
||
)
|
||
)
|
||
seen_edges.add(edge_id)
|
||
|
||
logger.info(
|
||
f"[{self.workspace}] Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
|
||
)
|
||
return result
|
||
|
||
async def get_all_nodes(self) -> list[dict]:
|
||
"""Get all nodes in the graph.
|
||
|
||
Returns:
|
||
A list of all nodes, where each node is a dictionary of its properties
|
||
"""
|
||
graph = await self._get_graph()
|
||
all_nodes = []
|
||
for node_id, node_data in graph.nodes(data=True):
|
||
node_data_with_id = node_data.copy()
|
||
node_data_with_id["id"] = node_id
|
||
all_nodes.append(node_data_with_id)
|
||
return all_nodes
|
||
|
||
async def get_all_edges(self) -> list[dict]:
|
||
"""Get all edges in the graph.
|
||
|
||
Returns:
|
||
A list of all edges, where each edge is a dictionary of its properties
|
||
"""
|
||
graph = await self._get_graph()
|
||
all_edges = []
|
||
for u, v, edge_data in graph.edges(data=True):
|
||
edge_data_with_nodes = edge_data.copy()
|
||
edge_data_with_nodes["source"] = u
|
||
edge_data_with_nodes["target"] = v
|
||
all_edges.append(edge_data_with_nodes)
|
||
return all_edges
|
||
|
||
async def index_done_callback(self) -> bool:
|
||
"""Commit in-memory graph to disk and notify other processes.
|
||
|
||
This is the writer's **commit point** in the cross-process sync
|
||
protocol (see class docstring). Two effects, in order:
|
||
1. ``write_nx_graph`` atomically writes the GraphML file
|
||
(``atomic_write`` swaps a tmp file into place).
|
||
2. ``set_all_update_flags`` flips every registered process's
|
||
``storage_updated`` flag, then we immediately reset our
|
||
own flag to ``False`` so the writer does not self-reload
|
||
on the next call to ``_get_graph``.
|
||
|
||
Two-block structure (intentional, do not collapse):
|
||
* **First ``async with``** — early-return path for a
|
||
hypothetical second writer. Under the current single-writer
|
||
pipeline contract (class docstring, invariant 1) the
|
||
``storage_updated.value`` check is permanently ``False`` in
|
||
the writer, so this branch is **dead code in production**.
|
||
It is kept as defensive scaffolding for any future
|
||
relaxation of the single-writer invariant; removing it
|
||
would silently re-enable lost-write bugs the moment a
|
||
second writer is introduced.
|
||
* **Second ``async with``** — the actual save + notify.
|
||
"""
|
||
async with self._storage_lock:
|
||
# Check if storage was updated by another process
|
||
if self.storage_updated.value:
|
||
# Storage was updated by another process, reload data instead of saving
|
||
logger.info(
|
||
f"[{self.workspace}] Graph was updated by another process, reloading..."
|
||
)
|
||
self._graph = (
|
||
NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
|
||
)
|
||
# Reset update flag
|
||
self.storage_updated.value = False
|
||
return False # Return error
|
||
|
||
# Acquire lock and perform persistence
|
||
async with self._storage_lock:
|
||
try:
|
||
# Save data to disk
|
||
NetworkXStorage.write_nx_graph(
|
||
self._graph, self._graphml_xml_file, self.workspace
|
||
)
|
||
# Notify other processes that data has been updated
|
||
await set_all_update_flags(self.namespace, workspace=self.workspace)
|
||
# Reset own update flag to avoid self-reloading
|
||
self.storage_updated.value = False
|
||
return True # Return success
|
||
except Exception as e:
|
||
# Raise (do NOT swallow + return False): _insert_done's
|
||
# _flush_one only detects failures via exceptions, so a
|
||
# swallowed graph-save error would let the document be marked
|
||
# PROCESSED with the graph changes unpersisted. Surfacing it
|
||
# aligns this backend with the others (faiss/nano raise too).
|
||
logger.error(f"[{self.workspace}] Error saving graph: {e}")
|
||
raise
|
||
|
||
return True
|
||
|
||
async def drop(self) -> dict[str, str]:
|
||
"""Drop all graph data from storage and reinitialize the graph.
|
||
|
||
This method will:
|
||
1. Remove the graph storage file if it exists
|
||
2. Reset the graph to an empty ``nx.Graph()``
|
||
3. Update flags to notify other processes
|
||
4. Changes are persisted to disk immediately
|
||
|
||
Caller contract:
|
||
``drop`` is destructive and **not** serialized by this storage
|
||
class. The caller must hold the pipeline ``busy`` reservation
|
||
(the ``/documents/clear`` endpoint does this) before invoking
|
||
it — running ``drop`` concurrently with an active document
|
||
pipeline will tear down storage out from under the writer and
|
||
silently lose data. See class docstring,
|
||
*Non-pipeline write paths*.
|
||
|
||
Returns:
|
||
dict[str, str]: Operation status and message
|
||
- On success: {"status": "success", "message": "data dropped"}
|
||
- On failure: {"status": "error", "message": "<error details>"}
|
||
"""
|
||
try:
|
||
async with self._storage_lock:
|
||
# delete _client_file_name
|
||
if os.path.exists(self._graphml_xml_file):
|
||
os.remove(self._graphml_xml_file)
|
||
self._graph = nx.Graph()
|
||
# Notify other processes that data has been updated
|
||
await set_all_update_flags(self.namespace, workspace=self.workspace)
|
||
# Reset own update flag to avoid self-reloading
|
||
self.storage_updated.value = False
|
||
logger.info(
|
||
f"[{self.workspace}] Process {os.getpid()} drop graph file:{self._graphml_xml_file}"
|
||
)
|
||
return {"status": "success", "message": "data dropped"}
|
||
except Exception as e:
|
||
logger.error(
|
||
f"[{self.workspace}] Error dropping graph file:{self._graphml_xml_file}: {e}"
|
||
)
|
||
return {"status": "error", "message": str(e)}
|